Alessandro Tricoli W → en + jets events By Rutherford-Appleton Laboratory In collaboration with Monika Wielers ATLAS-UK SM meeting, 26th February 2007
Alessandro Tricoli, RAL Overview Long term goal: W + jets cross section as function of jet multiplicity Immediate goal: set up analysis tools for CSC Note W/Z +jets check ATLAS electron and jet reconstruction performances estimate systematic uncertainties The only W->en +jets MC samples available now are SUSY samples (with very high filter cuts) however the generation of a SM sample has started. In this presentation Monte Carlo’s for multi-jet production: Alpgen W->en +jets event selection algorithm show basic distributions, main focus on jets and jet algorithms: Missing ET electron ET, h jet ET, jet multiplicity with different jet algorithms effect of jet energy scale uncertainty on jet multiplicity ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Multi-jet production SM multi-jet production processes , i.e W/Z + jets, important background to new physics searches. The large energies and luminosity at the LHC make final states with several hard and well separated jets a rather common phenomenon, (i.e. from hard QCD radiative processes, from decays of W, Z, top, Higgs, SUSY particles, etc.) Classical MC generators such as PYTHIA and HERWIG: parton-level M.E. calculations at LO then combine it with the partonic evolution given by the parton-shower (LL approx.). Other recent MC, such as MC@NLO: hard process calculations to NLO then combine it with the LL-parton shower evolution. Problem of matching between hard process and parton shower. In the case of large jet multiplicities, the complexity of the matrix element evaluation and of its singularity structure prevents the application of these approaches New strategies has been introduced for merging the exact matrix elements at the leading order in QCD and EW with the parton shower. ALPGEN CKKW method (applied on SHERPA MC) & ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alpgen Multi-parton hard processes MC’s in hadronic collisions: It performs the calculations for a large set of parton-level processes of interest for the LHC: For example W+1,2,3,4,5 parton samples are generated separately at LO. The interface to HERWIG or PYTHIA provides the treatment of higher-order correction (via parton-showers) and hadronisation. How to avoid double counting between samples and provide predictions for inclusive samples of arbitrary jet multiplicity? Jet-parton matching (MLM prescription) MLM (Michelangelo Mangano) prescription: Matching of matrix-element hard partons and shower generated jets: (for exclusive samples) a given a jet-algorithm the number of jets must be equal to the number of partons. ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Data Sets SUSY Alpgen + Jimmy: 4 samples exlcusive W → en + 2 parton, W → en + 3 partons, W → en + 4 partons inclusive W → en + 5 partons PDF: CTEQ6LL Reconstruction ATHENA v12.0.4 (and 12.0.3) Offline Analysis: AOD/AAN-tuples Very stringent generator filter cuts: 4 jets with pt > 40 GeV leading jet with pt > 80 GeV MissEt > 80 GeV Sample Dataset Sim rel Rec. rel. N. Gen. Events MLMxFilter Eff. (corr.) (pb) We+1parton NA We+2partons 5223 11.0.42 12.0.4 2,099 0.00133 0.67 We+3partons 5224 5,704 0.0278 3.39 We+4partons 5225 6,275 0.0710 2.02 We+5partons 5226 5,827 0.142 0.87 ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
W → en +jets Event Selection First identify events with high Et electron from W decay W → en event offline selection : Standard Electron Identification: isEM and cluster-track matching requirement cracks removal h=1.37-1.52 and |h|<2.4 Electron ET>25 GeV Missing-ET >25 GeV (redundant in this sample due to filter cuts) No jet veto cuts Then look at jets in the events Electron-Jet overlap removal: jets selected if there is no electron within DR<0.4 Minimum Jet Pt cut: due to high generator filter cuts, choice jet Pt>40 GeV (it will be lowered for SM samples) e n jet p Assumed luminosity = 100 pb-1 ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Electron ET, h distributions Compare reconstructed electrons after the W offline selection cuts to Generator level electrons coming from W decay (no cuts applied at generator level) 12.0.3 Bump due to W+2,3 partons events 12.0.3 Reconstructed Ele. After offline selection Generated electrons From W decay (no cuts) Electron Et (GeV) Electron h Dependency of electron identification eff. on number of jets Main concern ele isolation cuts (trigger and offline): the more jets in the events the more likely the electron is not isolated Dependency of the shape of electron distributions on the number of jets Investigation By Monika ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Missing ET and Missing ET Resolution MET_Truth MET_Final MET_RefFinal Cell-based: MET_Final (H1-style) MET_RefFinal (Refined calibration a la TDR: apply different weights to cells owning to different reconstructed objects (electrons, jets,...) ) ATHENA 12.0.4 Missing ET MET_Final MET_RefFinal ATHENA 12.0.4 Missing ET Resolution: (misETTruth – misETRec) Peak shift from MET_Final to MET_RefFinal misETTruth–misETRec ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Missing ET: Gaussian Fits MET_Final MET_RefFinal MET_RefFinal Distr. FITS: Mean = (5.57 ± 0.19) GeV = (14.74 ± 0.17) GeV c2 / Ndof = 3.34 MET_Final Mean = (0.09 ± 0.18) GeV = (14.17 ± 0.17) GeV c2 / Ndof = 3.05 MET_RefFinal MET_RefFinal Mean shift from MET_Final to MET_RefFinal by ~ -5.5 GeV MET_RefFinal now peaks at ~0 GeV Double gaussian needed to fit tails Resolution has only slightly improved from MET_Final to MET_RefFinal ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Missing ET Res. as function of Npartons MET_RefFinal Samples are not normalised here Np2 Np3 Np4 Np5 Mean 0.9±04 0.0±0.3 -0.0±0.3 0.3±0.4 s 12.4±0.3 13.4±0.2 14.6±0.3 17.7±0.4 GeV (As expected) Missing ET resolution deteriorated with increasing numbers of partons in the final state sigma increases by ~5 Gev from 2-parton to 5-parton events peak is constantly consistent with 0 (within stat uncertainties) ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Accompanying Jet Multiplicity Jet Pt > 7 GeV Jet Pt > 40 GeV CONE R=0.4 KT D=0.4 KT D=0.6 CONE R=0.7 CONE R=0.4 KT D=0.4 KT D=0.6 CONE R=0.7 Njets Njets Large discrepancy between Cone and KT algorithms is mainly due to high multiplicity of low Pt jets at high Pt CONE and KT algorithms give similar multiplicities ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Accompanying Jet Multiplicity Cumulative Multiplicity >= Njets Fit lines are exponential Jet Pt > 7 GeV Jet Pt > 40 GeV CONE R=0.4 KT D=0.4 KT D=0.6 CONE R=0.7 CONE R=0.4 KT D=0.4 KT D=0.6 CONE R=0.7 Multiplicity >= Njets Multiplicity >= Njets Large discrepancy between Cone and KT algorithms is mainly due to high multiplicity of low Pt jets after min pt cut KT and Cone are very close ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
How accurately can we measure the jet multiplicity? (thanks to Chiara Roda) Many sources of uncertainties: noise suppression, jet clustering, JES etc. At CDF the largest uncertainty is due to the Jet Energy Scale (JES) uncertainty Aim it to use ATLAS data to estimate JES we look at E/p of single hadrons, jet track multiplicity etc For the time being we use MC’s: question: how well the reconstructed jet multiplicity reproduces the multiplicity of the thuth-jets? comparisons between Detector and Generator levels ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Reconstructed vs Generated Jets: Jet multiplicity Detector level Generator level Detector level Generator level Jet Pt > 7 GeV CONE R=0.4 CONE R=0.7 Generator: Jet ET>7GeV |h|<5 Detector: Jet ET>7GeV |h|≤4.8 N jets N jets KT D=0.4 KT D=0.6 Truth: Jet ET>7GeV |h|<5 Detector: Jet ET>7GeV |h|≤4.8 N jets N jets Same jet algorithms and cuts applied on reconstructed and generated jets More jets at Generator wrt Detector level especially for CONE algorithms KT algorithms are closer to truth ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Reconstructed vs Generated Jets: Jet multiplicity Detector level Generator level CONE R=0.4 KT D=0.6 KT D=0.4 N jets Jet Pt > 40 GeV CONE R=0.7 Same jet algorithms and cuts applied on reconstructed and generated jets Better agreement between det. and gen. level multiplicities for higher Pt jets ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Jet ET Resolution varying Npartons (EtREC – EtMatch-Truth ) / EtMatch-Truth Jet Pt > 7 GeV Truth Jet Et (GeV) CONE R=0.4 CONE R=0.7 KT D=0.4 KT D=0.6 Np2 Np3 Np4 Np5 CONE R=0.7 and KT D=0.6 show flattest distributions ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Jet Energy Scale uncertainty Largest uncertainty at CDF Assume the uncertainty on Jet Energy Scale is 5% or 10%: The ET of each jet is miscalibrated by ±5% and ±10% wrt to our current best calibration For each jet in the event 4 samples are produced: for +5%, -5%, +10% and -10% jet ET miscalibrations +10% Et MisCal -10% Et Miscal +5% Et MisCal -5% Et Miscal (ETMisCalibration – ETStandardCalibration) / ETStandardCalibration Leading Jet Et (GeV) Here you see the ET miscalibration for the leading jet Generator filter cut: Leading Jet Pt > 80 GeV ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Effect of Jet Scale Uncertainty on Jet Multiplicity Jet Pt > 7 GeV Jet Pt > 40 GeV # Events ±10% ± 5% # Events KT D=0.4 KT D=0.4 N jets N jets If jet energy is miscalibrated by +5% or +10% (solid black and blue lines) Distribution shifted upwards, i.e. more events with larger jet multiplicity If jet energy is miscalibrated by -5% or -10% (dashed black and blue lines) Distribution shifted downwards, i.e. more events with smaller jet multiplicity ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Effect of Jet Scale Uncertainty on Cumulative Jet Multiplicity Jet Pt > 7 GeV Jet Pt > 40 GeV ±10% ± 5% # Events # Events KT D=0.4 KT D=0.4 N jets N jets Cross Section Uncertainty Cross Section Uncertainty ±10% ±5% ±10% ±5% KT D=0.4 KT D=0.4 N jets N jets Minimum in uncertainty (probably) due to tight filter cuts on SUSY samples.To be checked with SM sample. uncertainty on cross-section grows with Njets: from ~6% to ~50% at very high Njets ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Cross Section Uncertainty due to Jet Scale Uncertainty Samples & ET Miscal CDF(’98) Cone R=0.4 ATLAS KT D=0.4 KT D=0.6 Cone R=0.7 +D% -D% We+ ≥1 jet ±10% (±5%) 6.7 14.4 (6.3) 13.8 (6.4) 10.7 (5.2) 17.4 (9.1) 7.3 (3.5) We+ ≥2 jets ±10% (±5%) 10.1 11.5 13.3 (5.9) (6.1) 10.2 (5.1) 15.6 (8.4) 17.1 (8.9) 7.0 (3.4) 7.2 We+ ≥3 jets 12.8 16.7 6.3 (3.0) (4.3) 5.8 (3.6) 9.0 (4.0) 7.8 (4.7) 12.5 4.3 (2.5) We+ ≥4 jets 22.6 15.8 (7.1) 18.3 (8.5) 14.7 (7.8) (7.0) 16.6 (9.2) 20.4 (10.3) 12.4 13.7 (6.6) We+ ≥5 jets NA 21.4 (11.5) 22.3 (11.4) 20.8 (9.5) 20.5 (10.7) 24.7 (10.6) 22.4 20.2 18.9 We+ ≥6 jets 25.4 (14.3) 23.5 (11.8) 22.2 (10.4) 23.6 (11.9) 25.6 (12.8) 25.5 (14.1) 26.4 (11.1) ATLAS SM UK Meeting, 26th Feb. 2007 Jet Pt > 15 GeV Jet Pt > 40 GeV Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Conclusions Effect of the jet scale uncertainty on the W+jets cross section (to be redone with SM sample!!) is between ~ 3% (6%) and 14% (26%) for a jet energy scale uncertainty of 5% (10%) the dependency on the jet algorithm can be ~7% the challenge is to have a realistic estimate of the jet energy scale from data Missing ET: MET_RefFinal is an improvement wrt MET_Final Missing ET resolution deteriorated with higher parton multiplicity We have been developing tools to investigate W->en + jets events so far applied on SUSY samples will be applied to the SM samples when available ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Outlook Experimental studies: study JES with topocluster-jets: different clustering, noise suppression etc noise simulation and subtraction QCD background, UE subtraction etc Theoretical studies: In collaboration with Maria Fiascaris (Oxford student) and Mandy Cooper-Sarkar we want to estimate the PDF uncertainty on the jet multiplicity: is it larger or smaller than the experimental systematic uncertainties? if larger can we use these events to improve the PDF fits? (thanks to Chiara Roda) ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL EXTRAS ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL Multi-parton hard processes MC’s in hadronic collisions: Alpgen vs Sherpa New strategies has been introduced for merging the exact matrix elements at the leading order in QCD and EW with the parton shower. CKKW method (applied on SHERPA MC): This involves a re-weighting of the matrix element weights with Sudakov form factors (non-emission prob.), and the veto of shower emissions in regions of phase-space already covered by the parton-level configuration ALPGEN: Requires long generation time It performs the calculations for a large set of parton-level processes of interest for the LHC: For example W+1,2,3,4,5 parton samples are generated separately at LO. The interface to HERWIG or PYTHIA provides the treatment of higher-order correction (via parton-showers) and hadronisation. How to avoid double counting between samples and provide predictions for inclusive samples of arbitrary jet multiplicity? Jet-parton matching (MLM prescription) ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Alessandro Tricoli, RAL MLM prescription Matching of matrix-element hard partons and shower generated jets, following the so-called MLM prescription: given a jet-algorithm the number of jets must be equal to the number of partons. Events passing the matching criterion and having extra jets due to the parton shower evolution can be kept (inclusive mode) or rejected (exclusive mode). The inclusive mode must be used only for the sample with the highest parton multiplcity (e.g. the W+5 jet sample in our case). The set of showered events which survived the matching should be combined to obtain a fully inclusive result. For example for W + up to 5 jets, we generate exlusive W+1 parton, W+2 partons, W+3 partons, W+4 partons and inclusive W+5 partons Each of the individual samples will have its own absolute normalization. Since the definition of jet used by the matching prescription will most likely not coincide with the jet definition used by the user analysis, events from a given partonic multiplicity will result in events with a spectrum of jet multiplicities. ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Jet ET Resolution varing Npartons EtREC vs EtMatch-Truth Jet Pt > 7 GeV CONE R=0.4 CONE R=0.7 Np2 Np3 Np4 Np5 Np2 Np3 Np4 Np5 Reco Jet Et (GeV) Reco Jet Et (GeV) Truth Jet Et (GeV) Truth Jet Et (GeV) KT D=0.6 KT D=0.4 Np2 Np3 Np4 Np5 Np2 Np3 Np4 Np5 Reco Jet Et (GeV) Reco Jet Et (GeV) Truth Jet Et (GeV) Truth Jet Et (GeV) In this energy range there is no clear dependency on parton multiplicity ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Effect of Jet Scale Uncertainty on Jet Multiplicity Jet Pt > 7 GeV Jet Pt > 40 GeV Standard Cal. -10% Mis-Cal +10% Mis-Cal -5% Mis-Cal +5% Mis-Cal KT D=0.4 KT D=0.4 N jets N jets If jet energy is miscalibrated by +5% or +10% (solid black and blue lines) Distribution shifted upwards, i.e. more events with larger jet multiplicity If jet energy is miscalibrated by -5% or -10% (dashed black and blue lines) Distribution shifted downwards, i.e. more events with smaller jet multiplicity ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Effect of Jet Scale Uncertainty on cross-section of Leading Jet Et (ds/dETMisCalibration – ds/dETStandardCalibration) / ds/dETStandardCalibration Leading Jet Et (GeV) +10% -10% +5% -5% Cross Section Uncertainty Jet ET Miss-calibrations KT D=0.4 Asymmetric uncertainty upwards and downwards Uncertainty on W+jets cross section is larger for low ET leading jets (~100-200%) In the higher ET spectrum (~500 GeV) cross section uncertainty is about 50%, but stat is poor at high ET ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Jet Scale Uncertainty Summary All Jet algorithms have similar trends with a minimum at W + ≥3 jets this must be checked with SM samples with loser cuts Order of magnitude of cross-section uncertainty is similar to CDF We have to understand from data what is our jet scale uncertainty: 5%, 10% ? Changing jet algorithms the effect of the jet energy scale uncertainty on the cross section can vary by up to ±7% (especially at low multiplicities). An uncertainty on the jet energy scale of ±5% (±10%) determines an uncertainty on the cross section which grows with the multiplicity and is between ~ 3% (6%) and 14% (26%) (for jet multiplicities from ≥2 to ≥6 jets) ATLAS SM UK Meeting, 26th Feb. 2007 Alessandro Tricoli, RAL
Trigger efficiencies for W+jet events From Monika Use 11.0.42 simulated csc files for W+2, 3, 4, 5 jets generated with alpgen Datasets: 5223, 5224, 5225, 5226 For comparison use inclusive We generated with Pythia (DS 5104) Reconstruct using 12.0.3 + TriggerRelease-00-03-88 Trigger efficiencies for e25i normalised to electron with ET>25GeV in ||<1.37 or 1.52<||<2.47 at MC level Eff % Incl. W W+2j W+3j W+4j W+5j L1 98.3 92.4 91.9 78.5 76.9 L2 90.5 82.9 82.3 67.1 63.1 EF 80.2 72.3 72.0 58.3 54.5 Offline isem (no TRT cut) 69.0 68.4 55.0 51.8 EF (no L1 isol) 81.2 77.9 77.8 74.3 69.8 isem only 85.2 83.9 82.5 79.3 76.3 Alessandro Tricoli, Oxford University
Alessandro Tricoli, Oxford University Trigger efficiencies From Monika The more jets in the events the more likely the electron is not isolated L1 isolation rejects those events EM isolation in ring around 2x2 core ≤ 3 GeV Had core isolation in 2x2 towers behind EM core ≤ 2GeV Had isolation in ring around 2x2 core ≤ 2GeV Also other e-identification cuts (trigger and offline) which look at isolated electrons in ‘smaller’ region partly rejects those events as jet very near Alessandro Tricoli, Oxford University